Combining PLS regression with portable NIR spectroscopy to on-line monitor quality parameters in intact olives for determining optimal harvesting time

The experiment was conducted to find out the effect of variety, nitrogen level and harvesting time on yield and seed quality of barley. The treatments used in the experiment consisted of two varieties viz. BARI Barley 4 and BARI Barley 5, three harvesting time viz. 35, 40 and 45 Days after Anthesis (DAA) and nitrogen levels viz. 0, 70, 85 and 100 kg N ha-1 . The experiment was laid out in a spilt- spilt-plot design with three replications assigning the variety to the main plot, harvesting time to the sub-plots and nitrogen level to the sub-sub plots. Variety had significant effects on the all yield attributes except fertile seeds spike-1 . Seed quality parameters viz. normal seeds spike-1 , deformed seeds spike-1 , germination (%) and vigour index were statistically significant. The variety BARI Barley 5 produced higher grain yield and seed quality than BARI Barley 4. Grain yield from BARI Barley 5 and BARI Barley 4 were 4.59 t ha-1 and 4.24 t ha-1 , respectively. Significantly, the highest 1000-seed weight (46.90 g) was produced by BARI Barley 5 than (37.90 g) BARI Barley 4. The result revealed that harvesting time had significant effect on yield and yield attributes and seed quality parameters. Seed yield was highest (4.65 t ha-1 ) when the crop harvested at 40 DAA and it was increased linearly from 35 DAA. Maximum quality seed and 1000-seed weight (43.20 g) was obtained when the crop harvested at 40 DAA. All the yields, yield attributes and seed quality parameters were significantly influenced by nitrogen levels. The highest grain yield (5.14 t ha-1 ) was obtained when BARI Barley 5 variety was fertilized by 100 kg N ha-1 and the lowest (3.14 t ha-1 ) was obtained from control treatments. Normal seeds spike-1 , vigour index, germination (%) were better at 85 kg N ha-1 in variety of BARI Barley 5 than BARI Barley 4. So it can be concluded that BARI Barley 5 showed better result when fertilized with 100 kg N ha-1 and harvested at 40 DAA for getting maximum yield and 85 kg N ha-1 and harvested at 40 DAA for getting better quality seed.

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Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy

Sensors ◽

10.3390/s21062195 ◽

2021 ◽

Vol 21 (6) ◽

pp. 2195

Author(s):

Lucas de Paula Corrêdo ◽

Leonardo Felipe Maldaner ◽

Helizani Couto Bazame ◽

José Paulo Molin

Keyword(s):

Sample Preparation ◽

Prediction Models ◽

Spectral Response ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Partial Least Square ◽

Least Square ◽

Sample Type ◽

Sampling Strategies ◽

Significant Difference

Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.

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The Effect of Harvesting Time on Olive Fruits and Oils Quality Parameters of Tortiglione and Dritta Olive Cultivars

European Journal of Lipid Science and Technology ◽

10.1002/ejlt.202000382 ◽

2021 ◽

pp. 2000382

Author(s):

Flamminii Federica ◽

Marone Elettra ◽

Neri Lilia ◽

Pollastri Luciano ◽

Cichelli Angelo ◽

...

Keyword(s):

Quality Parameters ◽

Harvesting Time ◽

Olive Cultivars

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Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression ( i SPA-PLS)

Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy ◽

10.1016/j.saa.2017.08.046 ◽

2018 ◽

Vol 189 ◽

pp. 300-306 ◽

Cited By ~ 24

Author(s):

Gabriela Krepper ◽

Florencia Romeo ◽

David Douglas de Sousa Fernandes ◽

Paulo Henrique Gonçalves Dias Diniz ◽

Mário César Ugulino de Araújo ◽

...

Keyword(s):

Nir Spectroscopy ◽

Fat Content ◽

Pls Regression ◽

Successive Projections Algorithm ◽

Interval Selection ◽

Successive Projections

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Calibration modelling for non-destructive estimation of external and internal quality parameters of ‘Marsh’ grapefruit using Vis/NIR spectroscopy

Acta Horticulturae ◽

10.17660/actahortic.2018.1225.32 ◽

2018 ◽

pp. 233-238

Author(s):

O.O. Olarewaju ◽

L.S. Magwaza ◽

H.H. Nieuwoudt ◽

O.A. Fawole ◽

S.Z. Tesfay ◽

...

Keyword(s):

Nir Spectroscopy ◽

Quality Parameters ◽

Internal Quality ◽

Non Destructive

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Vis-NIR spectroscopy Combined with Wavelengths Selection by PSO Optimization Algorithm for Simultaneous Determination of Four Quality Parameters and Classification of Soy Sauce

Food Analytical Methods ◽

10.1007/s12161-018-01407-1 ◽

2018 ◽

Vol 12 (3) ◽

pp. 633-643 ◽

Cited By ~ 5

Author(s):

Leqian Hu ◽

Chunling Yin ◽

Shuai Ma ◽

Zhimin Liu

Keyword(s):

Simultaneous Determination ◽

Optimization Algorithm ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Soy Sauce

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Erratum to “Robust prediction models for quality parameters in Japanese plums (Prunus salicina L.) using NIR spectroscopy” [Postharvest Biol. Technol. 58 (2010) 176–184]

Postharvest Biology and Technology ◽

10.1016/j.postharvbio.2011.06.010 ◽

2011 ◽

Vol 62 (1) ◽

pp. 93-95

Author(s):

Esmé D. Louw ◽

Karen I. Theron

Keyword(s):

Prediction Models ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Prunus Salicina ◽

Robust Prediction

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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification

Journal of Spectroscopy ◽

10.1155/2018/8949741 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 8

Author(s):

Sylvio Barbon ◽

Ana Paula Ayub da Costa Barbon ◽

Rafael Gomes Mantovani ◽

Douglas Fernandes Barbin

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Nir Spectroscopy ◽

Quality Parameters ◽

Chicken Meat ◽

Poultry Meat ◽

Spectral Information ◽

Support Vector ◽

Near Infrared Spectra ◽

Meat Samples

Identification of chicken quality parameters is often inconsistent, time-consuming, and laborious. Near-infrared (NIR) spectroscopy has been used as a powerful tool for food quality assessment. However, the near-infrared (NIR) spectra comprise a large number of redundant information. Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. A combination of both attribute and wavelength selection for NIR spectral information of chicken meat samples was investigated. Decision Trees and Decision Table predictors exploit these optimal wavelengths for classification tasks according to different quality grades of poultry meat. The proposed methodology was conducted with a support vector machine algorithm (SVM) to compare the precision of the proposed model. Experiments were performed on NIR spectral information (1050 wavelengths), colour (CIEL∗a∗b∗, chroma, and hue), water holding capacity (WHC), and pH of each sample analyzed. Results show that the best method was the REPTree based on 12 wavelengths, allowing for classification of poultry samples according to quality grades with 77.2% precision. The selected wavelengths could lead to potential simple multispectral acquisition devices.

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